It’s no secret that Data Scientists have a difficult job. It feels like a lifetime ago that everyone was talking about data science as the sexiest job of the 21st century. Heck, it was so long ago that people were still meeting in person! Today, the sexy is starting to lose its shine. There’s recognition that it’s nearly impossible to find the unicorn data scientist that was the apple of every CEO’s eye in 2012.
As we get closer to the end of 2021, looking back the data and analytics technologies have evolved significantly over the last few years. We have seen the introduction of augmented analytics capabilities embracing user self-service and multimodal delivery of analytics insights. In addition, we have seen new data integration and catalog capabilities in response to the demands of new regulations and governance needs.
With the general availability of Cloudera DataFlow for the Public Cloud (CDF-PC), our customers can now self-serve deployments of Apache NiFi data flows on Kubernetes clusters in a cost effective way providing auto scaling, resource isolation and monitoring with KPI-based alerting. You can find more information in this release announcement blog post and in this technical deep dive blog post. Any customer willing to run NiFi flows efficiently at scale should now consider adopting CDF-PC.
At Snowday 2021, Snowflake announced exciting new product capabilities that expand what is possible in the Data Cloud. In addition to announcing Python support in Snowpark (currently in private preview), these latest innovations make it easier for organizations to maintain business continuity across clouds and regions; help data engineers and data scientists build pipelines, ML workflows, and data applications faster; and remove the complexity of getting the right data into the hands of customers.
Business intelligence (BI) has gotten so sophisticated that a variety of end users within an organization may be eager to use data to guide their decisions. Unfortunately, most businesses have a very small data analysis or BI team. How can companies like this enable more people to use more data more effectively without overwhelming their BI staff?
